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Found 27 papers, 7 papers with code
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases.
Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
In this paper, we address this research gap and introduce a deep learning algorithm for segmenting coronary arteries in multi-vendor ECG-gated non-contrast cardiac CT images which benefits from a novel framework for semi-automatic generation of Ground Truth (GT) via image registration.
Quantum Machine Learning for Remote Sensing: Exploring potential and challenges
In this paper, we investigate the application of QML in the field of remote sensing.
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
In this work, we consider extending classic SVMs with quantum kernels and applying them to satellite data analysis.
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers.
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis.
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 images
For Sentinel-2, spectral information fusion allows for enhancing the 20 m and 60 m bands to the 10 m resolution.
On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study
Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images.
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution
Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs.
Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma.
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data.
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment.
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands.
Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders
Hyperspectral image analysis has become an important topic widely researched by the remote sensing community.
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor.
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images
Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis.
On training deep networks for satellite image super-resolution
The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolutional neural networks.
Hyperspectral Data Augmentation
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks.
Deep Learning for Multiple-Image Super-Resolution
Recently, we have witnessed substantial improvement in single-image SRR attributed to the use of deep neural networks for learning the relation between low and high resolution.
Validating Hyperspectral Image Segmentation
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images.
A Parallel Memetic Algorithm to Solve the Vehicle Routing Problem with Time Windows
Here, the fleet size is minimized in the first phase of the proposed method using the parallel heuristic algorithm (PHA), and the traveled distance is minimized in the second phase by the parallel memetic algorithm (PMA).
Real-Time Hand Shape Classification
Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time.
